An improved particle swarm optimization algorithm for optimization of proportional–integral–derivative controller parameters

Abstract

The optimization of proportional-integral-derivative (PID) controller parameters poses a critical challenge in automatic control. Considering the complexity and uncertainty in actual systems, it is difficult to achieve desired dynamic performance, robustness, and stability at the same time. To overcome the inefficiency and low accuracy of conventional tuning methods, an improved particle swarm optimization (PSO) algorithm, logistic-tent Lévy PSO (LTLPSO), is proposed. This algorithm introduces logistic-tent chaotic mapping to initialize the population, enhance the uniformity and diversity of initial particles, and improve global exploration performance. In addition, the position update process integrates a Lévy flight strategy to help particles overcome local optima and improve the speed of convergence. The comparative evaluations with basic PSO, line-decreasing weight PSO, and chaotic mapping PSO confirm that LTLPSO has high convergence speed, accuracy, and stability. MATLAB/Simulink simulations are carried out on a delayed temperature system and a CNC-feed servo system to validate the performance of this algorithm. The results show that, compared with genetic algorithms and seeker optimization algorithms, the LTLPSO-PID controller can reduce the overshoot by up to 68% and the settling time by 30%, significantly improving dynamic and steady-state responses. This method can provide a robust and efficient solution for PID parameter tuning.